Brown bear optimized random forest model for short term solar power forecasting by Rathika Senthil Kumar, P.S. Meera, V. Lavanya, S. Hemamalini

“…In this paper, short-term solar power forecasting is done using random forest (RF) algorithm for a comparatively smaller data set. The results of the RF model are then compared with other ML models namely decision tree (DT), support vector regression (SVR), gradient boost (GB) and ridge regression (RR). …”
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Application Analysis of Credit Scoring of Financial Institutions Based on Machine Learning Model by Yi Wu, Yuwen Pan

“…On this basis, WOE coding was carried out on the dataset, which was applied to random forest, support vector machine, and logistic regression models, and the performance was compared. …”
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Prediction of Students’ Performance Based on the Hybrid IDA-SVR Model by Huan Xu

“…The aim of this study is to propose a novel intelligent approach to predict students’ performance using support vector regression (SVR) optimized by an improved duel algorithm (IDA). …”
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Prediction method of sugarcane important phenotype data based on multi-model and multi-task. by Jihong Sun, Chen Sun, Zhaowen Li, Ye Qian, Tong Li

“…In this study, we employed six key phenotypic traits of sugarcane, specifically plant height, stem diameter, third-node length (internode length), leaf length, leaf width, and field brix, along with eight machine learning methods: logistic regression, linear regression, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Backpropagation Neural Network (BPNN), Decision Tree, Random Forest, and the XGBoost algorithm. …”
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The Role of Performance Metrics in Estimating Market Values of Footballers in Europe's Top Five Leagues by Murat Işık, Mehmet Ali Yalçınkaya

“…Two main analytical methods were employed: regression and classification. In the regression analysis, seven models (Adaboost, Decision Tree, Gradient Boosting, K Nearest Neighbors, Random Forest, Ridge Regression, and Support Vector Machine) predicted players' market values. …”
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A Hybrid Forecasting System Based on Comprehensive Feature Selection and Intelligent Optimization for Stock Price Index Forecasting by Xuecheng He, Jujie Wang

“…At last, an enhanced least squares support vector machine (LSSVM) algorithm is established to obtain high-precision point prediction results, and the Gaussian process regression (GPR) algorithm is used to obtain reasonable interval prediction results. …”
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Regional stream temperature modeling in pristine Atlantic salmon rivers: A hybrid deterministic–Machine Learning approach by Ilias Hani, André St-Hilaire, Taha B.M.J. Ouarda

“…Additionally, a global sensitivity analysis is conducted to identify the most sensitive thermal parameters within the study region. We employed the support vector regression algorithm (SVR), to map the dependence of these parameters with climatic and watershed characteristics. …”
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Design of English Intelligent Simulated Paper Marking System by Lina Yang, Wei Liu

“…Statistically, it was found that the essay scores also showed a certain normal distribution. The standard support vector regression algorithm is prone to data skewing problems, so this paper addresses this problem by using a rationality enhancement method that gives a corresponding penalty factor according to the distribution of the dataset. …”
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HEAT TRANSFER AND FRICTION FACTOR OF FLAT PLATE SOLAR COLLECTOR WITH Al2O3 -CuO/WATER HYBRID NANOFLUIDS: EXPERIMENTAL AND ANN PREDICTIONS by Solomon Mesfin, Veeredhi Vasudeva Rao, L. Syam Sundar, Kotturu V.V. Chandra Mouli

“…Similarly, for time zone-1 and time zone-2, at 13:00 hrs and 16:30 hrs, at 0.24% vol. and at Reynolds number 364.66 and 211.23, the friction factor is enhanced by 15.34% and 11.50%, respectively, over the base fluid. The employed support vector regression algorithm accurately predicts the values with experimental data. …”
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Rapid and Nondestructive Identification of Origin and Index Component Contents of Tiegun Yam Based on Hyperspectral Imaging and Chemometric Method by Yue Zhang, Yuan Li, Cong Zhou, Junhui Zhou, Tiegui Nan, Jian Yang, Luqi Huang

“…Partial least squares regression (PLSR), random forest (RF), and support vector regression (SVR) models were used to predict the contents of starch, polysaccharide, and protein in Tiegun yam powder. …”
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Emotion Recognition in the Eye Region Using Textural Features, IBP and HOG by Laura Jalili, Josue Espejel, Jair Cervantes, Farid Lamont

“…Subsequently, we assessed the performance of multiple classifiers, including Support Vector Machine (SVM), Logistic Regression, Bayesian Regression, and Decision Trees, to identify the most effective approach. …”
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Machine Learning for Health Insurance Prediction in Nigeria by Victor Enemona Ochigbo, Oluwasogo Adekunle Okunade, Emmanuel Gbenga Dada, Oluyemi Mikail Olaniyi, Oluwatoyosi Victoria Oyewande

“…This paper focused on predicting the likelihood of medical insurance coverage among individuals in Nigeria by employing four prominent Machine learning techniques: Logistic Regression, Random Forest, Decision Tree, and Support Vector Machine classifiers. …”
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Komparasi Algoritma Klasifikasi Dengan Menggunakan Anaconda untuk Memprediksi Ramai Penonton Film di Bioskop by Rano Agustino

“…By using CART classification, NBC (Naive Bayes Classifier) algorithm, SVM (Support Vector Machine), LR (Logis Text Regression) and LDA (Linear Discriminant Analysis) which will be compared which accuracy is the best for predicting the absence or absence of the audience. …”
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Wheat yield prediction of Rajasthan using climatic and satellite data and machine learning techniques by KAVITA JHAJHARIA

“…In the present study, we implemented three machine learning algorithms, support vector regression, Random Forest and XGBoost, one linear regression method, Least Absolute Shrinkage and Selection Operator regression, and one deep learning method, long short-term memory, to predict the wheat yield prediction from 2008 to 2019 using satellite data (SIF) and vegetation indices. …”
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Predictive Analysis of Carbon Emissions in China’s Construction Industry Based on GIOWA Model by Tianyue Hu, Zhiheng Bao, Baiyang Zhang, Xinnan Gao

“…To accurately assess the carbon emission reduction potential of the construction industry and support the attainment of the dual carbon goals, this study constructs a generalized induced ordered weighted averaging (GIOWA) combination forecasting model, integrating support vector regression (SVR) and a long short-term memory neural network (LSTM). …”
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Sentiment Analysis of Netizens on Constitutional Court Rulings in the 2024 Presidential Election by Wahyudi Ariannor, Sami M A B Alshalwi, Budi Susarianto

“…This research explores sentiment analysis of the Constitutional Court’s decisions, especially in the context of the presidential election, using the Support Vector Machine (SVM), Logistic Regression, and Naive Bayes algorithms. …”
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Importance Analysis of Vegetation Change Factors in East Africa Based on Machine Learning by Zhang Xiumei, Ma Bo, Zhang Yijie

“…Six machine learning algorithms were used to establish NDVI prediction models: random forest (RF), BP neural networks (BP), support vector machines (SVM), genetic algorithm (GA), radial basis function (RBF), and convolutional neural networks (CNN). …”
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Acoustic Sensor Based Recognition of Human Activity in Everyday Life for Smart Home Services by Jae Mun Sim, Yonnim Lee, Ohbyung Kwon

“…The performance of the proposed algorithm was compared with that of commonly used classification algorithms such as case-based reasoning, k-nearest neighbors, support vector machine, and multiple regression.…”
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Research on the optimization method of inventory management of important spare parts of intercity railway. by Dongyan Wang, Ying Sun, Liang Yu, Kun Shen, Junbo Li, Xia Wu

“…To enhance the reliability of intercity railway operations and reduce spare parts management costs, this paper employs the Zebra Optimization Algorithm-Least Squares Support Vector Machine (ZOA-LSSVM) to analyze the reliability of the important Weibull distribution spare parts of the intercity railway and fit the parameters of the reliability function for spare parts. …”
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Romanian Fake News Detection Using Machine Learning and Transformer-Based Approaches by Elisa Valentina Moisi, Bogdan Cornel Mihalca, Simina Maria Coman, Alexandrina Mirela Pater, Daniela Elena Popescu

“…The NEW dataset was build using a scrapping algorithm applied on the Veridica platform. Our approach uses the following machine learning models for detection: Naive Bayes (NB), Logistic Regression (LR), and Support Vector Machine (SVM). …”
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